文章基本信息

标题：Indoor Positioning and Fingerprinting: The R Package ipft
本地全文：下载
作者：Emilio Sansano ; Raúl Montoliu ; Óscar Belmonte 等
期刊名称：R News
印刷版ISSN：1609-3631
出版年度：2019
卷号：11
期号：1
页码：67-90
语种：English
出版社：The R Foundation for Statistical Computing
摘要：Methods based on Received Signal Strength Indicator (RSSI) fingerprinting are in the forefront among several techniques being proposed for indoor positioning. This paper introduces the R package ipft, which provides algorithms and utility functions for indoor positioning using fingerprinting techniques. These functions are designed for manipulation of RSSI fingerprint data sets, estimation of positions, comparison of the performance of different positioning models, and graphical visualization of data. Well-known machine learning algorithms are implemented in this package to perform analysis and estimations over RSSI data sets. The paper provides a description of these algorithms and functions, as well as examples of its use with real data. The ipft package provides a base that we hope to grow into a comprehensive library of fingerprinting-based indoor positioning methodologies.